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IB Math Studies – Topic 6

IB Math Studies – Topic 6. Statistics. IB Course Guide Description. IB Course Guide Description. IB Course Guide Description. Categorical Data – Describes a particular quality or characteristic. It can be divided into categories. i.e. c olor of eyes or types of ice cream.

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IB Math Studies – Topic 6

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  1. IB Math Studies – Topic 6 Statistics

  2. IB Course Guide Description

  3. IB Course Guide Description

  4. IB Course Guide Description

  5. Categorical Data – Describes a particular quality or characteristic. It can be divided into categories. i.e. color of eyes or types of ice cream QuantitativeData – Contains a numerical value. The information collected is termed numerical data. Discrete – Takes exact number values and is often the result of counting. i.e. number of TVs or number of houses on a street Continuous – Takes numerical values within a certain range and is often a result of measuring. i.e. the height of seniors or the weight of freshman Describing Data Types of Data

  6. Types of Distribution Positively Skewed Distribution Symmetric Distribution Negatively Skewed Distribution

  7. Example 1: Describing Data 24 families were surveyed to find the number of people in the family. The results are:5, 9, 4, 4, 4, 5, 3, 4, 6, 8, 8, 5, 7, 6, 6, 8, 6, 9, 10, 7, 3, 5, 6, 6 • Is this data discrete or continuous? • Construct a frequency table for the data. • Display the data using a column graph. • Describe the shape of the distribution. Are there any outliers? • What percentage of families have 5 or fewer people in them?

  8. x is any score is the mean n is the number of scores Standard Deviation Formula

  9. Calculate the mean Subtract the mean from each value Square these Add them Divide by n Take the square root Calculate the standard deviation

  10. Type data in List 1 1-Var Stats L1 Standard Deviation on the GDC • On paper you’ll see ‘s’ being used to standard for standard deviation. • But you should use the σ measurement from the calculator.

  11. Measuring the Spread of Dara • The median is the second quartile, Q2 or 50th percentile • The lower quartile, Q1, is the median of the lower half of the data or 25th percentile • The upper quartile, Q3, is the median of the upper half of the data or 75th percentile • The inter-quartile range is the difference in the upper quartile and the lower quartile. IQR = Q3 – Q1

  12. Box Plots The inter-quartile range is the width of the box. The maximum length of each whisker is 1.5 times the inter-quartile range. Any data value that is larger than (or smaller than) 1.5 × IQR is marked as an outlier.

  13. To Create a Box-and-Whisker Plot: • Make a number line. • Create the box between Q1 and Q3. • Draw in Q2. • Determine any outliers: • Upper boundary = Q3 + 1.5(IQR) • Lower boundary = Q1 – 1.5(IQR) • Plot any outliers. • Extend the whiskers to the maximum & minimum (provided they’re not outliers).

  14. Example :Box and Whisker Plots A hospital is trialing a new anesthetic drug and has collected data on how long the new and old drugs take before the patient becomes unconscious. They wish to know which drug acts faster and which is more reliable. Old drug times: 8, 12, 9, 8, 16, 10, 14, 7, 5, 21, 13, 10, 8, 10 11, 8, 11, 9, 11, 14 New drug times: 8, 12, 7, 8, 12, 11, 9, 8, 10, 8, 10, 9, 12, 8, 8, 7, 10, 7, 9, 9 Prepare a parallel box plot for the data sets and use it to compare the two drugs for speed and reliability.

  15. FORMULA Pearson’s Correlation Coefficient: r

  16. Correlation Coefficient on the GDC • Turn on your Diagnostics • Enter the data in L1 and L2 • LinReg L1, L2

  17. Example 1: Correlation Coefficient In an experiment a vertical spring was fixed at its upper end. It was stretched by hanging different weights on its lower end. The length of the spring was then measured. The following readings were obtained. • (i) Write down the mean value of the load, • (ii) Write down the standard deviation of the load. • (iii) Write down the mean value of the length, • (iv) Write down the standard deviation of the length. It is given that the covariance Sxyis 12.17. (d)(i) Write down the correlation coefficient, r, for these readings. (ii) Comment on this result.

  18. Example 2: Correlation Coefficient Average speed in the metropolitan area and age of drivers The r-value for this association is 0.027. Describe the association.

  19. Drawing the Line of Best Fit • Calculate mean of x values , and mean of y values • Mark the mean point on the scatter plot • Draw a line through the mean point that is through the middle of the data • equal number of points above and below line

  20. Least Squares Regression Line • Consider the set of points below. • Square the distances and find their sum. • we want that sum to be small. • The regression line is used for prediction purposes. • The regression line is less reliable when extended far beyond the region of the data.

  21. Line of Regression using GDC • LinReg(ax +b) Test, L1, L2 • where L1 contains your independent data. • and L2 contains your dependent data

  22. Example 3: Line of Regression The table shows the annual income and average weekly grocery bill for a selection of families Construct a scatter plot to illustrate the data. Use technology to find the line of best fit. Estimate the weekly grocery bill for a family with an annual income of £95000. Comment on whether this estimate is likely to be reliable.

  23. X2 Test of Independence • The variables may be dependent: • Females may be more likely to exercise regularly than males. • The variables may be independent: • Gender has no effect on whether they exercise regularly. A chi-squared test is used to determine whether two variables from the same sample are independent.

  24. How to do it: • Write the null hypothesis (H0) and the alternate hypothesis (H1). • Create contingency tables for observed and expected values. • Calculate the chi-square statistic and degrees of freedom. • Find the chi-squared critical value (booklet). • Depends on the level of significance (p)and the degrees of freedom (v). • Determine whether or not to accept the null hypothesis.

  25. Contingency Tables • Observed Frequencies • Expected Frequencies

  26. Χ2 Statistic on the GDC On the calculator: Put your contingency table in matrix A • STAT • TESTS • C: χ2 Test • Observed: [A] • Expected: [B] (this is where you want to go) • Calculate • Output: • Χ2  Χ2 calculated value • df degrees of freedom • in Matrix B  expected values

  27. Find the Critical Value • Get this from the formula booklet. • Significance level (p) is always given in the problem. • A 5% significance level = 95% confidence level • Degrees of freedom: v = (c - 1)(r – 1) where c = number of columns in table and r = number of rows in table

  28. Accepting the Null Hypothesis If X2calc < Critical Value ACCEPT the null hypothesis If X2calc > Critical Value REJECT the null hypothesis

  29. Important IB Notes: • In examinations: the value of sxywill be given if required. • sxrepresents the standard deviation of the variable X; • sxyrepresents the covariance of the variables X and Y. • A GDC can be used to calculate r when raw data is given. • For the EXAM students do NOT need to know how to find the covariance. • But, for their project if they’re doing regression, then they DO need to do covariance by hand so they can do the r by hand so they can get points for using a sophisticated math process.

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